Technique for determining a cell-identity

ABSTRACT

A technique for cell-identity detection is provided. In one method embodiment, at least one cell-identity in a cellular telecommunication network is determined. The method comprises the steps of receiving a synchronization signal, calculating first correlations and second correlations as well as determining the at least one cell-identity. The received synchronization signal includes a first partial signal and a second partial signal. The first correlations are calculated between the first partial signal and first reference signals, each of which indicates one or more first cell-identities. The second correlations are calculated between the second partial signal and second reference signals, each of which indicates one or more cell-identities out of the first cell-identities. The second reference signals are selected depending on the first correlations. The cell-identity is determined based on the second correlations. The complexity for cell-identity detection is reduced by the technique.

RELATED APPLICATIONS

This application claims priority to EP 09015325.5, filed Dec. 10, 2009,and U.S. Provisional Application Ser. No. 61/292,906, filed Jan. 7,2010, both of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present disclosure relates to determining a cell-identity or acell-identity group in a cellular telecommunication network. Inparticular, the disclosure relates to determining one or morecell-identities by cross-correlation analysis.

BACKGROUND

Modern communication networks for mobile communication are organized incells. As one example, 3GPP Long Term Evolution (LTE) networks organizephysical-layer cell-identities in 168 unique physical-layercell-identity groups (represented by N_(ID) ⁽¹⁾=0, . . . , 167), eachcell-identity group N_(ID) ⁽¹⁾ comprising three cell-identities(represented by N_(ID) ⁽²⁾=0, 1, 2), which amounts to a total of 504cell-identities N^(CELL) _(ID)=3N_(ID) ⁽¹⁾+N_(ID) ⁽²⁾ addressable by apair of numbers N_(ID) ⁽¹⁾ and N_(ID) ⁽²⁾.

The cell-identity N_(ID) ⁽²⁾ is detected in a primary synchronizationsignal. For detection of the cell-identity group N_(ID) ⁽¹⁾, an analysisof a secondary synchronization signal (S-SSIG) X_(S-SSIG) involvescross-correlations with all possible S-SSIGs represented by referencesignals, each of which indicates a cell-identity group N_(ID) ⁽¹⁾. Alist of correlation peaks that exceed a threshold (and their correlationpeak values) is returned as cell-identity group candidates.

A first conventional technique directly implements a definition of thecorrelation,

$\begin{matrix}{{{c\left( {N_{ID}^{(1)},{s_{{pos},}{cp}_{type}}} \right)} = {\sum\limits_{n = 0}^{61}{{X_{S - {SSIG}}^{{cp}_{type}}(n)} \cdot {d_{N_{ID}^{(1)},s_{pos}}^{N_{ID}^{(2)}}(n)}}}},} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$to assess a set of communication parameters including the cell-identitygroup N_(ID) ⁽¹⁾, two possible subframe positions s_(pos) (at 0 ms or 5ms) and two possible cyclic prefix types cp_(type) (normal or extended).The received S-SSIG assuming a cyclic prefix type cp_(type) to be testedis denoted by X_(S-SSIG) ^(cp) ^(type) and the reference signal isdenoted by

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾).

The reference signal

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)is a sequence of elements in {−1, +1}, for which reason products in Eq.1 between elements of the synchronization signal X_(S-SSIG) ^(cp)^(type) (n) and the reference signal

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)(n)are not implemented by multiplication steps, but as fast sign changes.In the absence of multiplications steps, the computational complexitydepends on the number of involved addition steps.

Computation of the correlations requires considerable hardware resourcesas the synchronization signals occur twice per 10 ms radio frame, andthere are numerous combinations of receive signal data and referencesignals for each set of communication parameters to be evaluated. Thecomputational complexity of the above implementation requires2×2×168×62=41 664 steps of addition for evaluating each combination ofthe two cyclic prefix types (cp_(type)=0, 1), the two subframe timings(s_(pos)=0, 1), and the 168 possible cell-identity groups (N_(ID) ⁽¹⁾),each evaluation involving 62 additions for correlating correspondingsequences of length l=62. The conventional computation technique thusconsumes considerable battery resources and causes a notable cell searchtime.

A more advanced implementation exploits an alternating structure of thestandard definition of the reference signals

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾).Computation of the correlation c(N_(ID) ⁽¹⁾,s_(pos),cp_(type)) is splitup in two partial correlations of even-numbered elements with a firstreference signal

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)(2 n)and odd-numbered elements with a second reference signal

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)(2 n + 1)of length l/2=31:

${c\left( {N_{ID}^{(1)},s_{pos},{cp}_{type}} \right)} = {{\sum\limits_{n = 0}^{30}{{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {2\; n} \right)} \cdot {d_{N_{ID}^{(1)},s_{pos}}^{N_{ID}^{(2)}}\left( {2\; n} \right)}}} + {\sum\limits_{n = 0}^{30}{{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {{2\; n} + 1} \right)} \cdot {{d_{N_{ID}^{(1)},s_{pos}}^{N_{ID}^{(2)}}\left( {{2\; n} + 1} \right)}.}}}}$

According to the standard definition of 3GPP Technical Specification TS36.211 (V8.2.0, 2008-03) in Sect. 6.11.2.1 for the reference signals

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾),independently evaluable subsequences for the odd- and even-numberedelements of the reference signals

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)are:

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)(2 n) = s_(m_(x))(n) ⋅ c₀^(N_(ID)⁽²⁾)(n) = d_(m_(x))^(N_(ID)⁽²⁾)(2 n),wherein a first parameter m_(x)=m_(s) _(pos) (N_(ID) ⁽¹⁾) depends on thecommunication parameters cell-identity group, N_(ID) ⁽¹⁾, and thesubframe position, s_(pos); and

d_(N_(ID)⁽¹⁾, s_(pos))^(N_(ID)⁽²⁾)(2 n + 1) = s_(m_(y))(n) ⋅ c₁^(N_(ID)⁽²⁾)(n) ⋅ z_(m_(x)mod 8)(n) = d_(m_(x)mod 8, m_(y))^(N_(ID)⁽²⁾)(2 n + 1),wherein a second parameter is m_(y)=m_(1-s) _(pos) (N_(ID) ⁽¹⁾) and thefirst parameter m_(x)=m_(s) _(pos) (N_(ID) ⁽¹⁾) enters modulo 8.Integer-valued functions m₀(N_(ID) ⁽¹⁾) and m₁(N_(ID) ⁽¹⁾) are definedin document 3GPP TS 36.211.

Given the previously determined value of N_(ID) ⁽²⁾, there are M=31different first reference signals

d_(m_(x))^(N_(ID)⁽²⁾)parameterized by m_(x)=0, . . . , 30. Furthermore, there are 8×Mdifferent second reference signals

d_(m_(x)^(′), m_(y))^(N_(ID)⁽²⁾)parameterized by m′_(x)=0, . . . , 7 and m_(y)=0, . . . , 30. A pair(m_(x), m_(y)) of the first parameter m_(x) and the second parameterm_(y) (with m_(y)≠m_(x)) results from the correlation analysis. The pair(m_(x), m_(y)) uniquely determines the pair (N_(1D) ⁽¹⁾,s_(pos)) ofphysical-layer communication parameters: In case m_(x)<m_(y), s_(pos)=0and (m_(x), m_(y))=(m₀, m₁) determines the cell-identity group N_(ID)⁽¹⁾ according to Table 6.11.2.1-1 in 3GPP TS 36.211 (V8.2.0, 2008-03).Otherwise, if m_(x)>m_(y), s_(pos)=1 and (m_(y), m_(x))=(m₀, m₁)determines N_(ID) ⁽¹⁾.

Accordingly, there is a total of (1+8)×M first and second referencesignals, and the partial correlations with the receive signal X_(S-SSIG)^(cp) ^(type) for the two cyclic prefix types (cp_(type)) require addingup the l/2=31 elements, which amounts to 2×(1+8)×M×31 additions for eachpartial correlation. Adding the results of the two partial correlationsto obtain the (full) correlation for each of the 168×2×2 possible sets(N_(ID) ⁽¹⁾,s_(pos),cp_(type)) of communication parameters requires oneaddition for each set. Hence, the total number of additions is reducedto 2×9×31×31+168×2×2=17 970.

From “A New Cell Search Scheme in 3GPP Long Term Evolution DownlinkOFDMA Systems”, Wireless Communications & Signal Processing, 2009, acell search procedure in 3GPP LTE downlink systems is known. In a firststep of the procedure, a coarse symbol timing, and fractional CarrierFrequency Offset (CFO) is detected in order to process thefrequency-domain data. In a second step after acquiring the symboltiming, an integer CFO and a sector cell index N_(ID) ⁽²⁾ aresimultaneously detected by cross-correlating the receivedfrequency-domain data with Primary Synchronization Channel (P-SCH)signals. In a third step, the frame timing and cell-identity groupN_(ID) ⁽¹⁾ are detected using the Secondary Synchronization Channel(S-SCH) signal and the information about N_(ID) ⁽²⁾ deduced in thesecond step.

In US 2008/0273522 A1 various techniques for generating synchronizationsignals based on a M-sequence in order to convey cell parameters (e.g.,cell IDs) are taught. In one embodiment, the secondary synchronizationsignal is based on two cyclic shifted M-sequences with a length of N=31,wherein the cyclic shifts are indicative of the cell ID. The secondarysynchronization signal generated in such a way may be processed at auser terminal UE, in order to detect the cell-identity group N_(ID) ⁽¹⁾by using the cell index N_(ID) ⁽²⁾ and performing a M-sequence Transform(FMT) on two input sequences which correspond to those two M-sequencebased sequences of the generated synchronization signal.

SUMMARY

It is an object to further reduce the computational complexity of cellidentification.

According to a first aspect, the object is solved by a method ofdetermining at least one cell-identity in a cellular telecommunicationnetwork. The method comprises the steps of receiving a synchronizationsignal, calculating first correlations, calculating second correlations,and determining the at least one cell-identity therefrom. The receivedsynchronization signal includes a first partial signal and a secondpartial signal. The first correlations are calculated between the firstpartial signal and first reference signals, each of the first referencesignals indicating one or more first cell-identities. The secondcorrelations are calculated between the second partial signal and secondreference signals, each of the second reference signals indicating oneor more cell-identities out of the first cell-identities. The secondreference signals are selected depending on the first correlations. Thedetermination of the at least one cell-identity is based on the secondcorrelations. The term “cell-identity” as used herein encompasses anidentification of a single cell as well as an identification of a groupof cells.

The first cell-identities allow for a pre-selection depending on thefirst correlations prior to calculating the second correlations. Thispre-selection may reduce the complexity of remaining calculations. Byway of example, if there is a number of M₁ first reference signals (anda proportional number of 2×M₁ first correlations) and a total number ofM₂ possible second reference signals (and a proportional number of 2×M₂“conventional” second correlations without pre-selection), theconventional implementation involves 2×(M₁+M₂)×l/2 additions only forgenerating the first and second (partial) correlations. (In theintroductory example, M₁=M and M₂=8×M.) Selecting, depending on thefirst correlations, M′₂ second reference signals (with M′₂<M₂) out ofthe M₂ possible second reference signal can reduce the number ofadditions to 2×(M₁+M′₂)×l/2.

The selected second reference signal can correspond to a maximum of thefirst correlations or to N>1 highest first correlations. Alternatively,the selected second reference signals can correspond to those firstcorrelations which fulfill a specific threshold condition (e.g., exceeda predefined threshold value). A selection of the second referencesignals can stop after a predetermined number of second referencesignals has been selected. This allows for control over the number ofpossible cell-identity candidates and the required computationalcomplexity, particularly in real-time applications.

The first reference signals can be parameterized by a first parameterand the second reference signals can be parameterized by the firstparameter and a second parameter. A maximum of the first correlationscan determine the first parameter used in the calculation of the secondcorrelations. By continuation of the example, the number of M₁ firstreference signals can be parameterized by the first parameter and thenumber of M₂=k₁×k₂ possible second reference signals can beparameterized by a pair of the first parameter and the second parameter,the second parameter assuming m₂ different values or states (and thefirst parameter being mapped onto k₁<M₁ or k₁=M₁ values or sub-states).This allows rapidly selecting (and addressing) the second referencesignals by a choice of the first parameter depending on the firstcorrelations. The number of additions for generating the partialcorrelations can be reduced to 2×(M₁+k₂)×l/2.

The method may further comprise the step of generating a list ofdetermined cell-identities sorted according to a sum of a pair of firstcorrelation and second correlation, the pair corresponding to the listedcell-identity. The sums corresponding to each of the listedcell-identity candidates can provide a criterion or probability forapplying the corresponding listed cell-identity candidate.

The synchronization signal can be comprised of signal samples, whereinthe first partial signal comprises even-numbered signal samples and thesecond partial signal comprises odd-numbered signal samples. Such ascrambling of the partial signals can improve a tolerance to perturbingburst signals or an effectiveness of forward error correctiontechniques.

The selected second reference signals can further comprise a signalrepresentation of a previous (or current) cell-identity. This approachcan ensure continuity in the applied cell-identity. Furthermore, sincethe previous (or current) cell-identity may have a high probability ofbeing also best suited at a later time (depending on a speed of motionor a rate of cell changes), the cell search time can be shortened.

The determination of the at least one cell-identity can further dependon a code distance between at least one of the first and secondreference signal and one of the first partial signal, the second partialsignal, and the synchronization signal. Alternatively or in addition,the second reference signals can be selected depending on a codedistance between the first reference signal and the first partialsignal. The code distance can provide a further measure for an encesignal and the first partial signal. The code distance can provide afurther measure for an optimal and reliable determination of the atleast one cell-identity.

The generation of a (sorted) list of the determined cell-identitiesand/or the selection of second reference signals (for calculating thesecond correlations) may be terminated by a stop criterion depending onat least one of the first correlation, the second correlation, the sumof the first and second correlations, and the code distance. The codedistance measure is beneficial for sorting-out cell-identity candidateshaving a received synchronization signal (contribution) that exceeds acertain number of bit errors.

Generating at least one of the first reference signals and the secondreference signals can be based on a maximum length sequence (also knownas m-sequence). The calculation of at least one of the firstcorrelations and the second correlations can utilize a Modified HadamardTransformation (MHT) (based on the maximum length sequence). Correlationcalculations utilizing the MHT can be more efficient. While aconventional method of correlation computation may require M×l/2additions, the computational complexity of the MHT may scale asceil(log₂(M))×l/2, wherein P=ceil(log₂(M)) is the lowest integer P suchthat 2^(P)≧M. This may further reduce the cell search time and saveprocessor power, particularly for large M.

The first reference signals d_(m) _(x) (2n) can be generated by firstscrambling sequences, preferably as products of first scramblingsequences, according tod _(m) _(x) (2n)=s _(m) _(x) (n)·c ₀(n).

Second scrambling sequences, preferably products thereof, may generatethe second reference signals d_(m) _(x) _(,m) _(y) (2n+1) according tod _(m) _(x) _(,m) _(y) (2n+1)=s _(m) _(y) (n)·c ₁(n)·z _(m) _(x) (n).

Herein, sequences in n (on the right-hand side) can be the scramblingsequences. A pair of a first parameter m_(x) and a second parameterm_(y) can uniquely indicate one cell-identity. The pair of the first andsecond parameters may further indicate a slot position (for the receivesignal in the time domain). A representation of the first and/or secondreference signals in factors of scrambling sequences allows a fastreference signal generation.

The scrambling sequences can furthermore be represented as part of theMHT. The calculation of the first correlations can utilize the MHTaccording toc _(even)(m _(x))=MHT(X(2n)·c ₀(n)).

The calculation of the second correlations can utilize the MHT accordingtoc _(odd)(m _(x) ,m _(y))=MHT(X(2n+1)·c ₁(n)·z _(m) _(x) (n)).

This allows using the first and/or second parameter as a parameter ofthe MHT. Advantageously, one of the scrambling sequences, preferably ascrambling sequence depending on the first parameter and/or the secondparameter, can be eliminated by the MHT.

Each of the first and/or second reference signals can indicate (besidesone or more cell-identities) further communication parameters. Suchcommunication parameters can include a timing position for the receivesignal. Particularly, the communication parameters can include a slottiming and/or a cyclic prefix type (of the receive signal).

According to another aspect, the object is solved by a computer programproduct comprising program code for performing the steps of one ofaforementioned methods when the computer program product is executed onone or more computer devices. This allows a flexible and cost-effectiverealization. Advantageously, computational resources can be reallocateddepending on a state (online/offline) of a downlink connection (definedby the communication parameters). The object is also solved by acomputer-readable recording medium storing the computer program product.

According to a still further aspect, the object is solved by a devicefor determining at least one cell-identity in a cellulartelecommunication network. The device comprises a receiver and aprocessor. The receiver is adapted to receive a synchronization signalincluding a first partial signal and a second partial signal. Theprocessor is adapted to calculate first correlations between the firstpartial signal and first reference signals, wherein each of the firstreference signals indicate one or more first cell-identities. Theprocessor is further adapted to calculate second correlations betweenthe second partial signal and second correlation signals, wherein eachof the second reference signals indicate one or more cell-identities outof the first cell-identities. The processor or an Application SpecificIntegrated Circuit is also adapted to select the second reference signaldepending on the first correlations. The processor or a set of logicalgates is further adapted to determine the at least one cell-identitybased on the second correlations.

The selected second reference signals can correspond to a maximum of thefirst correlations or to N>1 highest first correlations.

The first reference signals can be parameterized by a first parameter.The second reference signals can be parameterized by the first parameterand a second parameter. For the selection of the second referencesignals, performed the processor depending on the first correlations, amaximum of the first correlations determines the (corresponding) firstparameter in the (subsequent) calculation of the second correlations.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment will now be discussed in greater detail with reference tothe drawings, in which:

FIG. 1 schematically shows a device embodiment;

FIG. 2 schematically shows a method embodiment, which can be performedby the device embodiment of FIG. 1;

FIG. 3 schematically shows functional components including acell-identity group detection component realized by the deviceembodiment of FIG. 1; and

FIG. 4 schematically shows further details of the cell-identity groupdetection component of FIG. 3.

DETAILED DESCRIPTION

In the following description, for purposes of explanation and notlimitation, specific details are set forth, such as individual steps forprocessing a certain receive signal, in order to provide a thoroughunderstanding of the current disclosure. It will be apparent to oneskilled in the art that the current technique may be practiced in otherembodiments that depart from these specific aspects.

Those skilled in the art will appreciate that functions explainedhereinbelow may be implemented using individual hardware circuitry,using software functioning in conjunction with a programmedmicroprocessor or a general purpose computer, using an ApplicationSpecific Integrated Circuit (ASIC) and/or using one or more DigitalSignal Processors (DSPs).

FIG. 1 shows an embodiment of a device 100 for determining a set ofcommunication parameters (“parameter set”) including (at least one)cell-identity group (denoted by N_(ID) ⁽¹⁾), a subframe position(denoted by s_(pos)=0, 1) at either 0 ms or 5 ms, and a cyclic prefixtype (denoted by cp_(type)) indicative of a normal or an extended cyclicprefix. The device is part of a receive stage in a mobile terminal, suchas a mobile telephone, a network card, a navigation device, or anetbook.

The device 100 comprises a processor 102 connected to a memory 104 and areceiver 106. The processor 102 and the receiver 106 are adapted toperform primary synchronization as well as secondary synchronization.

During primary synchronization, the device 100 receives a primarysynchronization signal (P-SSIG) to detect a physical-layer cell-identityN_(ID) ⁽²⁾ within the (yet unknown) cell-identity group. The P-SSIG alsoserves as a basis for an initial frequency offset estimation exploitingthe property of Zadoff-Chu sequences that, under frequency offset, thedetected peak timing is shifted. Correlation peaks for twelve possiblefrequency offsets are tested in combination with two polyphases of threepossible Zadoff-Chu sequences indicative of the cell-identity N_(ID)⁽²⁾=0, 1, 2. As a result of the 2×3×12=72 P-SSIG correlations, a list oftime position candidates is generated. After detection of thecell-identity N_(ID) ⁽²⁾, a coarse timing (for symbol, slot and frameposition) is performed.

During secondary synchronization, the physical-layer cell-identityN_(ID) ⁽²⁾, the coarse timing and the initial frequency offsetestimation are applied to analyze a secondary synchronization signal(S-SSIG). The S-SSIG is received according to step 202 of flow diagram200 (see FIG. 2) and undergoes preprocessing including low-passfiltering and channel estimation correction. The processor 102 isadapted to calculate first and second correlations for a first andsecond partial signals (included in the received synchronization signal)according to steps 204 and 206, respectively. The steps 202, 204, and206 are looped for averaging over several blocks of S-SSIG data toimprove a result on the correlations.

The first partial signal X(2n) comprises even-numbered elements of thesynchronization signal X(n). The first correlations are calculated foreach of a plurality of first reference signals d(2n) in the step 204.Each first reference signal d(2n) indicates a certain group of parametersets. The groups of parameter sets indicated by different firstreference signals are mutually disjoint. The first correlations serve asa pre-evaluation for candidate parameter sets among the groups ofparameter sets indicated by the first reference signals. The firstcorrelation, for example the square of its modulus, is used as anindication of the likelihood or suitability of the indicated group ofparameter sets.

After pre-selecting one or more groups of parameter sets out of thegroups indicated by the first reference signals based on thecorresponding first correlations, second correlations are calculated inthe step 206. The second correlations are calculated for a complementarysecond partial signal X(2n+1) of the synchronization signal X(n). Eachof the second correlations represents the correlation between the secondpartial signal X(2n+1) and one of a plurality of second referencesignals d(2n+1). Each of the second reference signals indicates one ormore parameter sets out of one of the pre-selected groups.

In step 208, at least one parameter set is determined based on thesecond correlations. Since the first and second partial signals arecomplementary to the synchronization and the calculation of thecorrelation is linear in the partial signals, the sum of the firstcorrelation and the second correlation equals a (complete) correlationof the (full) synchronization signal X(n) with respect to a combinedreference signal d(n) comprising the (alternating) elements of the firstand second reference signals. The (complete) correlation is a suitablemeasure indicating a parameter set, and in particular a cell-identity(or cell-identity group) regarding a quality of a communication link tothe cell-identity (or cell-identity group). Therefore, in an advancedembodiment the step 208 of determining is based on the (complete)correlation.

FIGS. 3 and 4 provide gradually increasing details of structural andfunctional features of the device embodiment 100 and the methodembodiment 200. Component diagram 300 in FIG. 3 shows two receivingantennas 302 and 304 of the device 100. Having more than one antennaallows for a compensation of fading in the communication link byadapting gain and weight of the antennas 302 and 304. In a reducedcomplexity embodiment, the second receiving antenna 304 is omitted.

An analog-to-digital conversion component 306 provides a digital datastream as a sequence of complex-valued amplitudes in the time domain forthe downlink connection. The analog-to-digital conversion component 306further performs low-pass filtering, and the digital signal isdown-sampled for further processing. A signal flow (indicated by arrowsbetween components of the diagram 300) includes parallel signalpropagation for two receive signals corresponding to the two antennas302 and 304.

The received signals include downlink physical signals such as downlinkreference signals and synchronization signals (SSIGs). Thesynchronization signals are split into the P-SSIG and the S-SSIG. Theprimary synchronization is performed by the P-SSIG correlation component308, which provides timing, frequency synchronization, and thecell-identity N_(ID) ⁽²⁾ based on the P-SSIG cross-correlation results.The P-SSIG correlation component 308 provides a list of possiblefrequency offsets to an initial frequency offset estimation component310, which processes the list to detect the initial frequency offset.The process is accomplished after a longer period of lost cellsynchronization or after switching-on the mobile terminal utilizing thepresent disclosure.

A cell-identity detection component 312 reads the correlations resultsof the P-SSIG correlation component 308 and generates a list ofcell-identities N_(ID) ⁽²⁾ together with respective timings

t_(cand)^(N_(ID)⁽²⁾).Based on a phase difference between the P-SSIG and the S-SSIG in thefrequency domain, a coarse frequency estimation is provided by a coarsefrequency estimation component 314. A timing component 316 (for OFDMsymbols, downlink slots, and radio frames) provides a refined timingbased on the S-SSIG. The S-SSIG is used to estimate a channel impulseresponse and to derive a timing tracking value.

A physical-layer cell-identity group detection component 318 determinesthe cell-identity group N_(ID) ⁽¹⁾ and the further parameters of slotposition, s_(pos), and cyclic prefix type, cp_(type). The cell-identitygroup detection component 318 uses the results of the components 312,314, and 316 as well as a potential initial frequency offset estimationprovided by the component 310 for the given N_(ID) ⁽²⁾. Thecell-identity group detection component 318 generates a list of validphysical-layer cell-identity groups N_(ID) ⁽¹⁾. The list is fed back tothe cell-identity detection component 312.

The structural and functional features of the cell-identity groupdetection component 318 are shown in more detail in FIG. 4. Thecell-identity group detection component 318 comprises a frequencycorrection subcomponent 320, a P-SSIG-based channel estimationsubcomponent 322, a cell-identity group correlation subcomponent 324 anda determination component 326. The determination component 326 furtherincludes a summation subcomponent 328 and a threshold subcomponent 330.

The potential initial frequency offset estimation is used by thefrequency correction subcomponent 320 to process samples r_(P-SSIG) andr_(S-SSIG) of the received signal flow. The output of the frequencycorrection subcomponent 320 includes the synchronization signalsX_(P-SSIG) and X_(S-SSIG). The frequency corrected signals 332 arebuffered for at least 5 ms to allow for channel estimation based on theX_(P-SSIG) 334 with channel estimate coefficients H 336 computed by thechannel estimation subcomponent 322. For Fourier transformation andFourier window positioning, the channel estimation subcomponent 322 usestiming signals and frequency signals provided by the timing component316 and the coarse frequency estimation component 314, respectively. Thephysical-layer cell-identity N_(ID) ⁽²⁾ is provided to the channelestimation subcomponent 322 and the cell-identity group correlationsubcomponent 324.

The cell-identity group correlation subcomponent 324 adds-up coherentlythe contributions from the two antennas 302 and 304 and calculates thepartial correlation for even-numbered and odd-numbered receive signalelements according to:

$\begin{matrix}{\mspace{79mu}{{{c_{even}\left( {m_{x},{cp}_{type}} \right)} = {\sum\limits_{n = 0}^{30}{{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {2\; n} \right)} \cdot {s_{m_{x}}(n)} \cdot {c_{0}(n)}}}},{and}}} & \left( {{Eq}.\mspace{14mu} 2} \right) \\{{c_{odd}\left( {{m_{x}{mod}\mspace{11mu} 8},m_{y},{cp}_{type}} \right)} = {\sum\limits_{n = 0}^{30}{{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {{2\; n} + 1} \right)} \cdot {s_{m_{y}}(n)} \cdot {c_{1}(n)} \cdot {{z_{m_{x}{mod}\; 8}(n)}.}}}} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

The cell-identity group correlation subcomponent 324 is run twice, oncefor normal and once for extended cyclic prefix data X_(S-SSIG) ^(cp)^(type) (cp_(type)=0, 1).

For each cyclic prefix type, the cell-identity group correlationsubcomponent 324 investigates the first (partial) correlation c_(even)(according to Eq. 2) to determine one or more first parameters m_(x),each of which is indicative of mutually disjoint groups ofphysical-layer parameter pairs (N_(ID) ⁽¹⁾,s_(pos)). A first parameterm_(x) ^(max) corresponding to the maximum of the first (partial)correlations c_(even)(m_(x), cp_(type)) for the given cyclic prefix typecp_(type) is determined and applied to the computation of the second(partial) correlations (according to Eq. 3). Alternatively, a list of Nfirst parameters yielding N highest first (partial) correlations aredetermined as the most probable candidates. Thereby, the number ofsecond (partial) correlations to be computed is reduced, which implies asignificant reduction in the number of addition steps required todetermine the maximum of the (complete) correlation c=c_(even)+c_(odd).

${\max\left( {c\left( {N_{ID}^{(1)},s_{pos},{cp}_{type}} \right)} \right)} = {{\max\left( {\sum\limits_{n = 0}^{30}{{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {2\; n} \right)} \cdot {s_{m_{x}}(n)} \cdot {c_{0}(n)}}} \right)} + {\max\limits_{m_{x} = m_{x}^{\max}}{\left( {\sum\limits_{n = 0}^{30}{{{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {{2\; n} + 1} \right)} \cdot {s_{m_{y}}(n)} \cdot {c_{1}(n)} \cdot z_{m_{x}{mod}\; 8}}(n)}} \right).}}}$

In a first embodiment, the maxima of odd and even partial correlationresults (represented as correlation vectors with indices m_(x) andm_(y)) are selected before adding the partial correlation results toobtain the (complete) correlation. By this, 335 out of 168×2=336additions can be saved (regarding the complexity of adding up the firstand second partial correlations).

In a second embodiment that is combinable with the first embodiment, notall eight groups of parameters indicated by an index m′_(x)=m_(x) mod 8are investigated. Rather, the number of calculation steps is furtherdecreased by selecting the N′ most probable (odd-numbered) secondreference signals (as indicated by the index m′_(x)), wherein theprobability is related to a sum of the first (partial) correlationresult corresponding the first parameter m_(x), such that m′_(x)=m_(x)mod 8 for the (given) index m′_(x). This reduces the number of additionsrequired for creating the (partial) correlation results to 31 additions(corresponding to the sequence length l/2) for each of the M=31 possiblereference sequences (m_(x/y)=0, . . . , 30), while there is one group ofM reference sequences on the even-numbered N_(ID) ⁽¹⁾ and N groups ofreference sequences on the odd numbered N_(ID) ⁽²⁾. Eventually, thecreation of the partial correlation results has to be repeated for eachof the two possible cyclic prefix types, such that the number ofaddition steps equals 2×(1+N)×31×31. In adding-up the two maxima of theodd and even partial correlation results there is one more addition foreach cyclic prefix type, such that the total number of additions isreduced to 2×(1+N)×31×31+2×1=3 846 for N=1 and using the maxima of thepartial correlation results only.

In a third embodiment, the advantages of the pre-selection according toany of above embodiments are combined with an efficient computation ofthe cross-correlations using a Modified Hadamard Transformation (MHT).By virtue of the recursive divide-and-conquer structure of the MHTcomputation, the complexity of calculating the partial correlations isreduced from 31² to log₂(32)×32 according to:c _(even)(m _(x) ,cp _(type))=MHT(X _(S-SSIG) ^(cp) ^(type) (2n)·c₀(n)), and  (Eq. 2′)c _(odd)(m _(x) mod 8,m _(y) ,cp _(type))=MHT(X _(S-SSIG) ^(cp) ^(type)(2n+1)·c ₁(n)·z _(m) _(x) _(mod 8)(n)).  (Eq. 3′)

Hence, the total number of additions is 2×(1+N)×log₂(32)×32+2×1=642 forN=1 and using the maxima of the partial correlation results only.

Similarly, the maximum of the first (partial) correlation or the Nhighest first (partial) correlations pre-determine the first parameterm_(x), based on which the evaluation of the second (partial)correlations yields the second parameter m_(y) and the cyclic prefixtype cp_(type). The physical-layer communication parameters (N_(ID)⁽¹⁾,s_(pos)) follow from the pair of first and second parameters (m_(x),m_(y)).

The maximum of the (complete) correlation is thus:

${\max\left( {c\left( {N_{ID}^{(1)},s_{pos},{cp}_{type}} \right)} \right)} = {{\max\left( {{MHT}\left( {{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {2\; n} \right)} \cdot {c_{0}(n)}} \right)} \right)} + {\max\limits_{m_{x} = m_{x}^{\max}}{\left( {{MHT}\left( {{X_{S\text{-}{SSIG}}^{{cp}_{type}}\left( {{2\; n} + 1} \right)} \cdot {c_{1}(n)} \cdot {z_{m_{x}{mod}\; 8}(n)}} \right)} \right).}}}$

In an alternative embodiment, instead of analyzing all of the eightgroups of second (partial) correlations indicated by m′_(x)=m_(x) mod 8,a stop criterion is defined to terminate the search for the mostprobable N′ parameter groups out of the eight possible parameter groups.Once one or N′>1 correlation values greater or equal to the stopcriterion have been pre-selected, the search is terminated.

In a further alternative embodiment, the stop criterion is based on acode distance measure, or a combination of correlation value and codedistance. A preferred code distance measure applies a Hamming metric. Apreferred stop criterion or threshold condition based on (partial)correlation values uses a maximum (partial) correlation value from aprevious successfully verified cell-identity (for example the currentlyserving cell) multiplied by a threshold factor α. In this embodiment, aequals 0.75.

The cell-identity group detection component 318 generates as output (tothe cell-identity detection component 312) a sorted list of thecommunication parameter sets. A sorting criterion is the (complete)correlation or a combination of correlation values and code distancemeasure.

In order to further speed-up the processing, the list furthermore startswith the currently serving cell-identity or the N′ highest-rankedparameter sets of a previous cell search.

As has become apparent from the above embodiments, the techniquepresented herein significantly reduces the calculation complexityinvolved in cell searches, which reduces an energy consumption of amobile terminal applying the technique, increases a battery runtime andreduces its processing delay. The technique allows high rates of cellidentification (at a given limit of computation resources), whichincreases the quality of the communication link and increases aneffective data throughput rate.

While specific embodiments have been shown and described above, variouschanges, modifications and substitutions may be made without departingfrom the scope of the present disclosure.

What is claimed is:
 1. A method of determining at least onecell-identity in a cellular telecommunication network, the methodcomprising: receiving a synchronization signal including a first partialsignal and a second partial signal; calculating first correlationsbetween the first partial signal and first reference signals each of thefirst reference signals indicating one or more first cell-identities;calculating second correlations between the second partial signal andsecond reference signals each of the second reference signals indicatingone or more cell-identities out of the one or more firstcell-identities, wherein the second reference signals are selecteddepending on the first correlations; and determining the at least onecell-identity based on the second correlations.
 2. The method of claim1, wherein the selected second reference signals correspond to a maximumof the first correlations or to N>1 highest first correlations.
 3. Themethod of claim 1, wherein the selected second reference signalscorrespond to first correlations fulfilling a threshold condition. 4.The method of claim 1, wherein the selection of the second referencesignals stops after a predefined number of second reference signals hasbeen selected.
 5. The method of claim 1, wherein the first referencesignals are parameterized by a first parameter and the second referencesignals are parameterized by the first parameter and a second parametera maximum of the first correlations determining the first parameter inthe calculation of the second correlations.
 6. The method of claim 1,further comprising generating a list of the determined at least onecell-identities that is sorted according to the sum of the firstcorrelation and the second correlation corresponding to each of thelisted cell-identities.
 7. The method of claim 1, wherein thesynchronization signal is comprised of signal samples, the first partialsignal comprising even-numbered signal samples and the second partialsignal comprising odd-numbered signal samples.
 8. The method of claim 1,wherein the selected second reference signals further comprise a signalrepresentative of a previous or current cell-identity.
 9. The method ofclaim 1, wherein the determination of the at least one cell-identityfurther depends on a code distance between at least one of the first andsecond reference signal and one of the first partial signal, the secondpartial signal, and the synchronization signal.
 10. The method of claim1, wherein at least one of the first reference signals and the secondreference signals includes a maximum length sequence and the calculationof at least one of the first correlations and the second correlationsapplies a modified Hadamard transformation (MHT).
 11. The method ofclaim 1, wherein first scrambling sequences, s_(m) _(x) , c₀, generatethe first reference signal defined as d_(m) _(x) (2n) according tod _(m) _(x) (2n)=s _(m) _(x) (n)·c ₀(n) and second scrambling sequences,s_(m) _(y) , c₁, z_(m) _(x) , generate the second reference signaldefined as d_(m) _(x) _(,m) _(y) (2n+1) according tod _(m) _(x) _(,m) _(y) (2n+1)=s _(m) _(y) (n)·c ₁(n)·z _(m) _(x) (n), apair of a first parameter, m_(x), and a second parameter, m_(y),uniquely indicating one cell-identity.
 12. The method of claim 11,wherein at least one of the first reference signals and the secondreference signals includes a maximum length sequence and the calculationof at least one of the first correlations and the second correlationsapplies a modified Hadamard transformation (MHT), and wherein the firstcorrelations equalc _(even)(m _(x))=MHT(X(2n)·c ₀(n)) and the second correlations equalc _(odd)(m _(x) ,m _(y))=MHT(X(2n+1)·c ₁(n)·z _(m) _(x) (n)), whereinX(2n) represents the first partial signal and X(2n+1) represents thesecond partial signal.
 13. The method of claim 1, wherein each of thefirst reference signals and/or each of the second reference signalsfurther indicates at least one of a timing position and a cyclic prefixtype of the received signal.
 14. A computer program product stored on anon-transitory, computer-readable recording medium and comprisingprogram code that, when executed on a computer system, cause thecomputer system to determine at least one cell-identity in a cellulartelecommunication network, wherein the computer system is associatedwith a receiver for receiving a synchronization signal including a firstpartial signal and a second partial signal and further wherein theprogram code causes the computer system to: calculate first correlationsbetween the first partial signal and first reference signals each of thefirst reference signals indicating one or more first cell-identities;calculate second correlations between the second partial signal andsecond reference signals each of the second reference signals indicatingone or more cell-identities out of the one or more firstcell-identities, wherein the second reference signals are selecteddepending on the first correlations; and determine the at least onecell-identity based on the second correlations.
 15. A device fordetermining at least one cell-identity in a cellular telecommunicationnetwork, the device comprising: a receiver configured to receive asynchronization signal including a first partial signal and a secondpartial signal; and a processor configured to: calculate firstcorrelations between the first partial signal and first referencesignals each of the first reference signals indicating one or more firstcell-identities; calculate second correlations between the secondpartial signal and second reference signals each of the second referencesignals indicating one or more cell-identities out of the one or morefirst cell-identities, wherein the second reference signals are selecteddepending on the first correlations; and determine the at least onecell-identity based on the second correlations.
 16. The device of claim15, wherein the selected second reference signals correspond to amaximum of the first correlations or to N>1 highest first correlations.17. The device of claim 15, wherein the first reference signals areparameterized by a first parameter and the second reference signals areparameterized by the first parameter and a second parameter a maximum ofthe first correlations determining the first parameter in thecalculation of the second correlations.
 18. The device of claim 15,wherein the selected second reference signals correspond to firstcorrelations fulfilling a threshold condition.
 19. The device of claim15, wherein the selection of the second reference signals stops after apredefined number of second reference signals has been selected.
 20. Thedevice of claim 15, wherein the processor is further configured togenerate a list of the determined at least one cell-identities that issorted according to the sum of the first correlation and the secondcorrelation corresponding to each of the listed cell-identities.